AWS provides Amazon SageMaker Pipelines as the native CI/CD solution for ML. Pipelines can automatically trigger retraining when new data arrives in Amazon S3, handle large datasets (such as 10 GB files), and orchestrate preprocessing, training, evaluation, and deployment steps.

For governance and auditing, Amazon SageMaker Model Registry integrates directly with Pipelines to manage model versions, approval status, and metadata such as training metrics. This combination eliminates the need for custom tracking logic and ensures traceability across the ML lifecycle.

Option A lacks native ML lineage and version governance. Option C is unsuitable for large data and complex workflows. Option D is manual and not scalable.

Therefore, using SageMaker Pipelines with the Model Registry is the correct and AWS-recommended solution.